Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations5242
Missing cells14805
Missing cells (%)10.5%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory3.1 MiB
Average record size in memory625.2 B

Variable types

Text4
Numeric20
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
52 Weeks High is highly overall correlated with 52 Weeks Low and 7 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
Chiffre d'affaires is highly overall correlated with 52 Weeks High and 11 other fieldsHigh correlation
Currency is highly overall correlated with Chiffre d'affaires and 4 other fieldsHigh correlation
Dividend Per Share Annual is highly overall correlated with 52 Weeks Low and 5 other fieldsHigh correlation
EBITDA is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with PriceHigh correlation
Price is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks Low and 5 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
Total assets is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Currency is highly imbalanced (90.8%)Imbalance
P/E Ratio has 2523 (48.1%) missing valuesMissing
Beta has 377 (7.2%) missing valuesMissing
Performance (52 weeks) has 206 (3.9%) missing valuesMissing
Chiffre d'affaires has 620 (11.8%) missing valuesMissing
Résultat net has 142 (2.7%) missing valuesMissing
Sector has 56 (1.1%) missing valuesMissing
Industry has 56 (1.1%) missing valuesMissing
Price 52 Weeks Ago has 177 (3.4%) missing valuesMissing
Total assets has 160 (3.1%) missing valuesMissing
EPS Annual has 72 (1.4%) missing valuesMissing
Dividend Per Share Annual has 2699 (51.5%) missing valuesMissing
EBITDA CAGR (5y) has 2769 (52.8%) missing valuesMissing
EBITDA has 1134 (21.6%) missing valuesMissing
ROI Annual has 101 (1.9%) missing valuesMissing
Ratio Debt/Equity (Annual) has 139 (2.7%) missing valuesMissing
Dividend Yield Indicated Annual has 3454 (65.9%) missing valuesMissing
Price is highly skewed (γ1 = 28.50315029)Skewed
Market Cap (in M) is highly skewed (γ1 = 26.1092932)Skewed
P/E Ratio is highly skewed (γ1 = 34.05333567)Skewed
Volume 52 weeks is highly skewed (γ1 = 46.11408214)Skewed
Volume 1 month is highly skewed (γ1 = 28.67854287)Skewed
52 Weeks High is highly skewed (γ1 = 36.29613085)Skewed
52 Weeks Low is highly skewed (γ1 = 24.59222217)Skewed
Chiffre d'affaires is highly skewed (γ1 = 67.22226507)Skewed
Résultat net is highly skewed (γ1 = -71.38377006)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 35.53222318)Skewed
EPS Annual is highly skewed (γ1 = -39.02390396)Skewed
Dividend Per Share Annual is highly skewed (γ1 = 50.02265371)Skewed
EBITDA CAGR (5y) is highly skewed (γ1 = 49.7267385)Skewed
EBITDA is highly skewed (γ1 = -63.76650563)Skewed
ROI Annual is highly skewed (γ1 = -54.81442802)Skewed
Ratio Debt/Equity (Annual) is highly skewed (γ1 = 36.32714722)Skewed
Dividend Per Share Annual has 467 (8.9%) zerosZeros
Ratio Debt/Equity (Annual) has 1074 (20.5%) zerosZeros

Reproduction

Analysis started2024-08-12 12:14:39.861490
Analysis finished2024-08-12 12:15:18.745737
Duration38.88 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

Distinct5204
Distinct (%)100.0%
Missing38
Missing (%)0.7%
Memory size309.3 KiB
2024-08-12T14:15:18.960681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6029977
Min length1

Characters and Unicode

Total characters18750
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5204 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowZCMD
5th rowMOVE
ValueCountFrequency (%)
trns 1
 
< 0.1%
snax 1
 
< 0.1%
colm 1
 
< 0.1%
zcmd 1
 
< 0.1%
move 1
 
< 0.1%
nmih 1
 
< 0.1%
gnta 1
 
< 0.1%
allr 1
 
< 0.1%
rrbi 1
 
< 0.1%
cndt 1
 
< 0.1%
Other values (5194) 5194
99.8%
2024-08-12T14:15:19.328549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1240
 
6.6%
S 1206
 
6.4%
R 1176
 
6.3%
N 1083
 
5.8%
I 961
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1240
 
6.6%
S 1206
 
6.4%
R 1176
 
6.3%
N 1083
 
5.8%
I 961
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1240
 
6.6%
S 1206
 
6.4%
R 1176
 
6.3%
N 1083
 
5.8%
I 961
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1240
 
6.6%
S 1206
 
6.4%
R 1176
 
6.3%
N 1083
 
5.8%
I 961
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%
Distinct5205
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size399.6 KiB
2024-08-12T14:15:19.566701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length76
Median length54
Mean length21.033384
Min length2

Characters and Unicode

Total characters110257
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5204 ?
Unique (%)99.3%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowZhongchao Inc
5th rowMovano Inc
ValueCountFrequency (%)
inc 3141
 
18.6%
corp 896
 
5.3%
ltd 451
 
2.7%
holdings 374
 
2.2%
group 288
 
1.7%
fund 268
 
1.6%
co 193
 
1.1%
income 192
 
1.1%
therapeutics 186
 
1.1%
trust 159
 
0.9%
Other values (5263) 10759
63.6%
2024-08-12T14:15:19.917722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11665
 
10.6%
n 9437
 
8.6%
e 7479
 
6.8%
o 6794
 
6.2%
c 6399
 
5.8%
i 6236
 
5.7%
r 6205
 
5.6%
a 6094
 
5.5%
t 5158
 
4.7%
s 4230
 
3.8%
Other values (64) 40560
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11665
 
10.6%
n 9437
 
8.6%
e 7479
 
6.8%
o 6794
 
6.2%
c 6399
 
5.8%
i 6236
 
5.7%
r 6205
 
5.6%
a 6094
 
5.5%
t 5158
 
4.7%
s 4230
 
3.8%
Other values (64) 40560
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11665
 
10.6%
n 9437
 
8.6%
e 7479
 
6.8%
o 6794
 
6.2%
c 6399
 
5.8%
i 6236
 
5.7%
r 6205
 
5.6%
a 6094
 
5.5%
t 5158
 
4.7%
s 4230
 
3.8%
Other values (64) 40560
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11665
 
10.6%
n 9437
 
8.6%
e 7479
 
6.8%
o 6794
 
6.2%
c 6399
 
5.8%
i 6236
 
5.7%
r 6205
 
5.6%
a 6094
 
5.5%
t 5158
 
4.7%
s 4230
 
3.8%
Other values (64) 40560
36.8%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3682
Distinct (%)70.6%
Missing30
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean46.063046
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:20.035752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.55109
Q13.45
median12.07
Q338.9375
95-th percentile183.215
Maximum8506.24
Range8506.1897
Interquartile range (IQR)35.4875

Descriptive statistics

Standard deviation169.38741
Coefficient of variation (CV)3.6772951
Kurtosis1259.4627
Mean46.063046
Median Absolute Deviation (MAD)10.52
Skewness28.50315
Sum240080.59
Variance28692.095
MonotonicityNot monotonic
2024-08-12T14:15:20.139408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2821 38
 
0.7%
1.16 9
 
0.2%
1.02 8
 
0.2%
1.65 8
 
0.2%
1.4 8
 
0.2%
1.04 8
 
0.2%
1.06 8
 
0.2%
1.01 8
 
0.2%
1.1 7
 
0.1%
1.47 7
 
0.1%
Other values (3672) 5103
97.3%
(Missing) 30
 
0.6%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5201
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10922.963
Minimum0
Maximum3287742.5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:20.241355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.8470457
Q190.571067
median541.19488
Q33213.0706
95-th percentile38082.25
Maximum3287742.5
Range3287742.5
Interquartile range (IQR)3122.4996

Descriptive statistics

Standard deviation89359.146
Coefficient of variation (CV)8.1808524
Kurtosis807.91229
Mean10922.963
Median Absolute Deviation (MAD)523.92255
Skewness26.109293
Sum57258171
Variance7.985057 × 109
MonotonicityNot monotonic
2024-08-12T14:15:20.345041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.11919333 38
 
0.7%
0 3
 
0.1%
203.84 2
 
< 0.1%
44.22 2
 
< 0.1%
1051.552692 1
 
< 0.1%
101.819299 1
 
< 0.1%
187.13 1
 
< 0.1%
1420.384117 1
 
< 0.1%
1300.036875 1
 
< 0.1%
2651.83368 1
 
< 0.1%
Other values (5191) 5191
99.0%
ValueCountFrequency (%)
0 3
0.1%
0.135 1
 
< 0.1%
0.202462083 1
 
< 0.1%
0.290874472 1
 
< 0.1%
0.3662907246 1
 
< 0.1%
0.5117001326 1
 
< 0.1%
0.6849636886 1
 
< 0.1%
0.6859719465 1
 
< 0.1%
0.727851 1
 
< 0.1%
0.7453370722 1
 
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

P/E Ratio
Real number (ℝ)

MISSING  SKEWED 

Distinct2713
Distinct (%)99.8%
Missing2523
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean60.361278
Minimum0.0062
Maximum21839.557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:20.455264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0062
5-th percentile3.10504
Q110.0497
median18.5133
Q334.7327
95-th percentile127.46075
Maximum21839.557
Range21839.551
Interquartile range (IQR)24.683

Descriptive statistics

Standard deviation513.07186
Coefficient of variation (CV)8.5000165
Kurtosis1328.882
Mean60.361278
Median Absolute Deviation (MAD)10.1041
Skewness34.053336
Sum164122.32
Variance263242.74
MonotonicityNot monotonic
2024-08-12T14:15:20.657478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4093 2
 
< 0.1%
15.2663 2
 
< 0.1%
16.1532 2
 
< 0.1%
10.3985 2
 
< 0.1%
4.4923 2
 
< 0.1%
5.372 2
 
< 0.1%
3.0741 1
 
< 0.1%
8.1228 1
 
< 0.1%
60.109 1
 
< 0.1%
13.0303 1
 
< 0.1%
Other values (2703) 2703
51.6%
(Missing) 2523
48.1%
ValueCountFrequency (%)
0.0062 1
< 0.1%
0.0203 1
< 0.1%
0.0302 1
< 0.1%
0.0406 1
< 0.1%
0.0578 1
< 0.1%
0.1099 1
< 0.1%
0.1138 1
< 0.1%
0.1795 1
< 0.1%
0.1814 1
< 0.1%
0.1884 1
< 0.1%
ValueCountFrequency (%)
21839.5571 1
< 0.1%
12604.5649 1
< 0.1%
3923.2138 1
< 0.1%
3507.735 1
< 0.1%
2956.059 1
< 0.1%
2646.6441 1
< 0.1%
2163.2004 1
< 0.1%
2088.6392 1
< 0.1%
1789.4863 1
< 0.1%
1524.9309 1
< 0.1%

Beta
Real number (ℝ)

MISSING 

Distinct4828
Distinct (%)99.2%
Missing377
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean1.0547108
Minimum-9.529384
Maximum47.4254
Zeros0
Zeros (%)0.0%
Negative521
Negative (%)9.9%
Memory size41.1 KiB
2024-08-12T14:15:20.755645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9.529384
5-th percentile-0.30310412
Q10.41645133
median0.9131638
Q31.5485908
95-th percentile2.9387
Maximum47.4254
Range56.954784
Interquartile range (IQR)1.1321395

Descriptive statistics

Standard deviation1.3577471
Coefficient of variation (CV)1.2873169
Kurtosis292.84921
Mean1.0547108
Median Absolute Deviation (MAD)0.5561027
Skewness9.2304202
Sum5131.1682
Variance1.8434772
MonotonicityNot monotonic
2024-08-12T14:15:20.853439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87638956 38
 
0.7%
0.9319894 1
 
< 0.1%
0.76148 1
 
< 0.1%
1.289463 1
 
< 0.1%
0.93625027 1
 
< 0.1%
0.90106916 1
 
< 0.1%
0.8942454 1
 
< 0.1%
0.990637 1
 
< 0.1%
0.023227256 1
 
< 0.1%
0.21630187 1
 
< 0.1%
Other values (4818) 4818
91.9%
(Missing) 377
 
7.2%
ValueCountFrequency (%)
-9.529384 1
< 0.1%
-6.9775743 1
< 0.1%
-6.976032 1
< 0.1%
-5.8663783 1
< 0.1%
-5.5417604 1
< 0.1%
-5.3560877 1
< 0.1%
-4.9759016 1
< 0.1%
-4.6895614 1
< 0.1%
-4.5976734 1
< 0.1%
-4.5330396 1
< 0.1%
ValueCountFrequency (%)
47.4254 1
< 0.1%
19.570795 1
< 0.1%
15.053838 1
< 0.1%
11.313087 1
< 0.1%
10.333447 1
< 0.1%
9.652831 1
< 0.1%
9.578695 1
< 0.1%
8.625989 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5201
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1387086.6
Minimum0
Maximum4.6049593 × 108
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:20.959013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12497.341
Q186673.909
median349861.31
Q31083377.8
95-th percentile5088915
Maximum4.6049593 × 108
Range4.6049593 × 108
Interquartile range (IQR)996703.87

Descriptive statistics

Standard deviation7455448.1
Coefficient of variation (CV)5.3748974
Kurtosis2757.5377
Mean1387086.6
Median Absolute Deviation (MAD)311472.02
Skewness46.114082
Sum7.2711079 × 109
Variance5.5583706 × 1013
MonotonicityNot monotonic
2024-08-12T14:15:21.061197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291535.3175 38
 
0.7%
6648.015873 2
 
< 0.1%
82251.5873 2
 
< 0.1%
0 2
 
< 0.1%
225453.9683 2
 
< 0.1%
78841.66667 1
 
< 0.1%
13591270.63 1
 
< 0.1%
273965.0794 1
 
< 0.1%
801371.4286 1
 
< 0.1%
25511.11111 1
 
< 0.1%
Other values (5191) 5191
99.0%
ValueCountFrequency (%)
0 2
< 0.1%
156.5737052 1
< 0.1%
251.984127 1
< 0.1%
329.7619048 1
< 0.1%
343.484127 1
< 0.1%
364.0355731 1
< 0.1%
665.0873016 1
< 0.1%
777.7777778 1
< 0.1%
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5118
Distinct (%)97.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1567375.5
Minimum0
Maximum3.4773365 × 108
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:21.163465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8573.913
Q168804.348
median331656.52
Q31160115.7
95-th percentile6105630.4
Maximum3.4773365 × 108
Range3.4773365 × 108
Interquartile range (IQR)1091311.3

Descriptive statistics

Standard deviation6825485.1
Coefficient of variation (CV)4.3547222
Kurtosis1302.4378
Mean1567375.5
Median Absolute Deviation (MAD)304904.35
Skewness28.678543
Sum8.2146152 × 109
Variance4.6587247 × 1013
MonotonicityNot monotonic
2024-08-12T14:15:21.266732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51391.30435 39
 
0.7%
81686.95652 3
 
0.1%
52026.08696 3
 
0.1%
23152.17391 3
 
0.1%
606143.4783 2
 
< 0.1%
4356.521739 2
 
< 0.1%
1244960.87 2
 
< 0.1%
2143.478261 2
 
< 0.1%
307469.5652 2
 
< 0.1%
18326.08696 2
 
< 0.1%
Other values (5108) 5181
98.8%
ValueCountFrequency (%)
0 2
< 0.1%
4.545454545 1
< 0.1%
17.39130435 1
< 0.1%
47.82608696 1
< 0.1%
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
125.0416667 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3864
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.7658
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:21.376814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.5
Q18.5
median18.53
Q352.9875
95-th percentile227.65374
Maximum14400
Range14399.13
Interquartile range (IQR)44.4875

Descriptive statistics

Standard deviation270.32817
Coefficient of variation (CV)4.3766642
Kurtosis1724.7247
Mean61.7658
Median Absolute Deviation (MAD)13.78
Skewness36.296131
Sum323776.33
Variance73077.319
MonotonicityNot monotonic
2024-08-12T14:15:21.477405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.75 40
 
0.8%
14 9
 
0.2%
4 8
 
0.2%
13 8
 
0.2%
12 8
 
0.2%
2.05 8
 
0.2%
11.85 7
 
0.1%
12.5 7
 
0.1%
2.1 7
 
0.1%
3.6 7
 
0.1%
Other values (3854) 5133
97.9%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3639
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.842796
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:21.583786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.40715
Q12.2325
median9.705
Q327.8775
95-th percentile134.164
Maximum5210.49
Range5210.4896
Interquartile range (IQR)25.645

Descriptive statistics

Standard deviation113.13881
Coefficient of variation (CV)3.4448593
Kurtosis948.62729
Mean32.842796
Median Absolute Deviation (MAD)8.455
Skewness24.592222
Sum172161.94
Variance12800.391
MonotonicityNot monotonic
2024-08-12T14:15:21.688219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2801 38
 
0.7%
0.75 13
 
0.2%
1 11
 
0.2%
0.65 10
 
0.2%
1.21 9
 
0.2%
1.5 9
 
0.2%
1.04 9
 
0.2%
0.7 9
 
0.2%
1.1 9
 
0.2%
1.42 8
 
0.2%
Other values (3629) 5117
97.6%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.0131 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0767 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size318.6 KiB
NASDAQ
3192 
NYSE
2048 

Length

Max length6
Median length6
Mean length5.2183206
Min length4

Characters and Unicode

Total characters27344
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 3192
60.9%
NYSE 2048
39.1%
(Missing) 2
 
< 0.1%

Length

2024-08-12T14:15:21.786092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:15:21.883643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 3192
60.9%
nyse 2048
39.1%

Most occurring characters

ValueCountFrequency (%)
A 6384
23.3%
N 5240
19.2%
S 5240
19.2%
D 3192
11.7%
Q 3192
11.7%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6384
23.3%
N 5240
19.2%
S 5240
19.2%
D 3192
11.7%
Q 3192
11.7%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6384
23.3%
N 5240
19.2%
S 5240
19.2%
D 3192
11.7%
Q 3192
11.7%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6384
23.3%
N 5240
19.2%
S 5240
19.2%
D 3192
11.7%
Q 3192
11.7%
Y 2048
 
7.5%
E 2048
 
7.5%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4991
Distinct (%)99.1%
Missing206
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.0085887105
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2477
Negative (%)47.3%
Memory size41.1 KiB
2024-08-12T14:15:21.966518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.85228985
Q1-0.34820409
median0.010099031
Q30.21014827
95-th percentile0.76468541
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.55835236

Descriptive statistics

Standard deviation0.76752702
Coefficient of variation (CV)-89.36464
Kurtosis715.18945
Mean-0.0085887105
Median Absolute Deviation (MAD)0.25833254
Skewness17.97954
Sum-43.252746
Variance0.58909772
MonotonicityNot monotonic
2024-08-12T14:15:22.168407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7696988475 38
 
0.7%
0.387608379 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
-0.8042474026 2
 
< 0.1%
-0.5009512193 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
-0.1670839982 2
 
< 0.1%
-0.7109845435 2
 
< 0.1%
0.07029265643 2
 
< 0.1%
0.4630629347 1
 
< 0.1%
Other values (4981) 4981
95.0%
(Missing) 206
 
3.9%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9966251513 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct52
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size302.2 KiB
2024-08-12T14:15:22.287967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10484
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowCN
5th rowUS
ValueCountFrequency (%)
us 4399
83.9%
cn 243
 
4.6%
il 86
 
1.6%
gb 66
 
1.3%
ca 60
 
1.1%
hk 51
 
1.0%
sg 48
 
0.9%
bm 32
 
0.6%
ie 31
 
0.6%
ky 25
 
0.5%
Other values (42) 201
 
3.8%
2024-08-12T14:15:22.481207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4457
42.5%
U 4431
42.3%
C 332
 
3.2%
N 260
 
2.5%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4457
42.5%
U 4431
42.3%
C 332
 
3.2%
N 260
 
2.5%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4457
42.5%
U 4431
42.3%
C 332
 
3.2%
N 260
 
2.5%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4457
42.5%
U 4431
42.3%
C 332
 
3.2%
N 260
 
2.5%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.3%

Chiffre d'affaires
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4565
Distinct (%)98.8%
Missing620
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean1.3370507 × 1010
Minimum-3.66072 × 108
Maximum3.4011754 × 1013
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.3%
Memory size41.1 KiB
2024-08-12T14:15:22.585837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.66072 × 108
5-th percentile1943041.6
Q154345936
median4.3610686 × 108
Q32.3595636 × 109
95-th percentile1.9523509 × 1010
Maximum3.4011754 × 1013
Range3.401212 × 1013
Interquartile range (IQR)2.3052176 × 109

Descriptive statistics

Standard deviation5.0213225 × 1011
Coefficient of variation (CV)37.555213
Kurtosis4551.0369
Mean1.3370507 × 1010
Median Absolute Deviation (MAD)4.27379 × 108
Skewness67.222265
Sum6.1798485 × 1013
Variance2.5213679 × 1023
MonotonicityNot monotonic
2024-08-12T14:15:22.689382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78335376 38
 
0.7%
500000 4
 
0.1%
6591000064 3
 
0.1%
124000 2
 
< 0.1%
22233000 2
 
< 0.1%
10000000 2
 
< 0.1%
85000 2
 
< 0.1%
143768992 2
 
< 0.1%
3500000 2
 
< 0.1%
7093000192 2
 
< 0.1%
Other values (4555) 4563
87.0%
(Missing) 620
 
11.8%
ValueCountFrequency (%)
-366072000 1
< 0.1%
-130852000 1
< 0.1%
-98696000 1
< 0.1%
-75971000 1
< 0.1%
-65000000 1
< 0.1%
-59345000 1
< 0.1%
-38133000 1
< 0.1%
-21382000 1
< 0.1%
-11137000 1
< 0.1%
-9598000 1
< 0.1%
ValueCountFrequency (%)
3.401175374 × 10131
< 0.1%
2.223509078 × 10121
< 0.1%
9.41168001 × 10111
< 0.1%
6.68366209 × 10111
< 0.1%
6.573319782 × 10111
< 0.1%
6.043339981 × 10111
< 0.1%
3.856030106 × 10111
< 0.1%
3.854390067 × 10111
< 0.1%
3.618549924 × 10111
< 0.1%
3.451349893 × 10111
< 0.1%

Résultat net
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5022
Distinct (%)98.5%
Missing142
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean-1.0750059 × 1010
Minimum-5.7899787 × 1013
Maximum9.3358398 × 1011
Zeros0
Zeros (%)0.0%
Negative2345
Negative (%)44.7%
Memory size41.1 KiB
2024-08-12T14:15:22.799332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.7899787 × 1013
5-th percentile-2.7801 × 108
Q1-34705000
median3077677.5
Q31.178485 × 108
95-th percentile1.7669966 × 109
Maximum9.3358398 × 1011
Range5.8833371 × 1013
Interquartile range (IQR)1.525535 × 108

Descriptive statistics

Standard deviation8.1088196 × 1011
Coefficient of variation (CV)-75.430467
Kurtosis5097.1231
Mean-1.0750059 × 1010
Median Absolute Deviation (MAD)61422464
Skewness-71.38377
Sum-5.48253 × 1013
Variance6.5752955 × 1023
MonotonicityNot monotonic
2024-08-12T14:15:22.901326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-254352048 38
 
0.7%
1052000000 3
 
0.1%
714000000 3
 
0.1%
21383000 2
 
< 0.1%
-8167000 2
 
< 0.1%
-40105000 2
 
< 0.1%
650000000 2
 
< 0.1%
-9636000 2
 
< 0.1%
-1122000 2
 
< 0.1%
1560000000 2
 
< 0.1%
Other values (5012) 5042
96.2%
(Missing) 142
 
2.7%
ValueCountFrequency (%)
-5.789978722 × 10131
< 0.1%
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-7966970880 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
ValueCountFrequency (%)
9.335839785 × 10111
< 0.1%
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%

Sector
Categorical

MISSING 

Distinct11
Distinct (%)0.2%
Missing56
Missing (%)1.1%
Memory size360.2 KiB
Financial Services
1168 
Healthcare
1052 
Technology
750 
Industrials
574 
Consumer Cyclical
536 
Other values (6)
1106 

Length

Max length22
Median length18
Mean length13.497686
Min length6

Characters and Unicode

Total characters69999
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowHealthcare

Common Values

ValueCountFrequency (%)
Financial Services 1168
22.3%
Healthcare 1052
20.1%
Technology 750
14.3%
Industrials 574
11.0%
Consumer Cyclical 536
10.2%
Real Estate 245
 
4.7%
Consumer Defensive 215
 
4.1%
Communication Services 210
 
4.0%
Energy 184
 
3.5%
Basic Materials 162
 
3.1%

Length

2024-08-12T14:15:23.005496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services 1378
17.8%
financial 1168
15.1%
healthcare 1052
13.6%
consumer 751
9.7%
technology 750
9.7%
industrials 574
7.4%
cyclical 536
 
6.9%
real 245
 
3.2%
estate 245
 
3.2%
defensive 215
 
2.8%
Other values (5) 808
10.5%

Most occurring characters

ValueCountFrequency (%)
e 7932
11.3%
a 6736
 
9.6%
i 6053
 
8.6%
c 5792
 
8.3%
n 5230
 
7.5%
l 5113
 
7.3%
s 4151
 
5.9%
r 4101
 
5.9%
o 2671
 
3.8%
t 2668
 
3.8%
Other values (21) 19552
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7932
11.3%
a 6736
 
9.6%
i 6053
 
8.6%
c 5792
 
8.3%
n 5230
 
7.5%
l 5113
 
7.3%
s 4151
 
5.9%
r 4101
 
5.9%
o 2671
 
3.8%
t 2668
 
3.8%
Other values (21) 19552
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7932
11.3%
a 6736
 
9.6%
i 6053
 
8.6%
c 5792
 
8.3%
n 5230
 
7.5%
l 5113
 
7.3%
s 4151
 
5.9%
r 4101
 
5.9%
o 2671
 
3.8%
t 2668
 
3.8%
Other values (21) 19552
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7932
11.3%
a 6736
 
9.6%
i 6053
 
8.6%
c 5792
 
8.3%
n 5230
 
7.5%
l 5113
 
7.3%
s 4151
 
5.9%
r 4101
 
5.9%
o 2671
 
3.8%
t 2668
 
3.8%
Other values (21) 19552
27.9%

Industry
Text

MISSING 

Distinct145
Distinct (%)2.8%
Missing56
Missing (%)1.1%
Memory size387.8 KiB
2024-08-12T14:15:23.176976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length34
Mean length19.202661
Min length4

Characters and Unicode

Total characters99585
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowHealth Information Services
5th rowMedical Devices
ValueCountFrequency (%)
2341
 
17.7%
biotechnology 602
 
4.6%
services 466
 
3.5%
management 452
 
3.4%
asset 437
 
3.3%
software 367
 
2.8%
banks 327
 
2.5%
regional 322
 
2.4%
specialty 305
 
2.3%
medical 241
 
1.8%
Other values (189) 7330
55.6%
2024-08-12T14:15:23.470940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9967
 
10.0%
8004
 
8.0%
i 7061
 
7.1%
t 7018
 
7.0%
a 6844
 
6.9%
n 6804
 
6.8%
o 5601
 
5.6%
s 5338
 
5.4%
r 4822
 
4.8%
c 4436
 
4.5%
Other values (38) 33690
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9967
 
10.0%
8004
 
8.0%
i 7061
 
7.1%
t 7018
 
7.0%
a 6844
 
6.9%
n 6804
 
6.8%
o 5601
 
5.6%
s 5338
 
5.4%
r 4822
 
4.8%
c 4436
 
4.5%
Other values (38) 33690
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9967
 
10.0%
8004
 
8.0%
i 7061
 
7.1%
t 7018
 
7.0%
a 6844
 
6.9%
n 6804
 
6.8%
o 5601
 
5.6%
s 5338
 
5.4%
r 4822
 
4.8%
c 4436
 
4.5%
Other values (38) 33690
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9967
 
10.0%
8004
 
8.0%
i 7061
 
7.1%
t 7018
 
7.0%
a 6844
 
6.9%
n 6804
 
6.8%
o 5601
 
5.6%
s 5338
 
5.4%
r 4822
 
4.8%
c 4436
 
4.5%
Other values (38) 33690
33.8%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4068
Distinct (%)80.3%
Missing177
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean46.685642
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:23.584032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.36
Q16.0013294
median13.5
Q338.990002
95-th percentile170.63462
Maximum11250
Range11249.7
Interquartile range (IQR)32.988672

Descriptive statistics

Standard deviation213.2488
Coefficient of variation (CV)4.56776
Kurtosis1664.4797
Mean46.685642
Median Absolute Deviation (MAD)10.463427
Skewness35.532223
Sum236462.77
Variance45475.052
MonotonicityNot monotonic
2024-08-12T14:15:23.684852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.220000029 46
 
0.9%
10.69999981 8
 
0.2%
1.679999948 8
 
0.2%
2.049999952 8
 
0.2%
4.199999809 7
 
0.1%
3.019999981 7
 
0.1%
12 7
 
0.1%
3.779999971 6
 
0.1%
5 6
 
0.1%
10.47000027 6
 
0.1%
Other values (4058) 4956
94.5%
(Missing) 177
 
3.4%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)0.5%
Missing49
Missing (%)0.9%
Memory size307.1 KiB
USD
4930 
CNY
 
147
EUR
 
33
BRL
 
12
CAD
 
11
Other values (19)
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15579
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4930
94.0%
CNY 147
 
2.8%
EUR 33
 
0.6%
BRL 12
 
0.2%
CAD 11
 
0.2%
HKD 10
 
0.2%
GBP 9
 
0.2%
SGD 6
 
0.1%
AUD 6
 
0.1%
JPY 5
 
0.1%
Other values (14) 24
 
0.5%
(Missing) 49
 
0.9%

Length

2024-08-12T14:15:23.775662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4930
94.9%
cny 147
 
2.8%
eur 33
 
0.6%
brl 12
 
0.2%
cad 11
 
0.2%
hkd 10
 
0.2%
gbp 9
 
0.2%
sgd 6
 
0.1%
aud 6
 
0.1%
jpy 5
 
0.1%
Other values (14) 24
 
0.5%

Most occurring characters

ValueCountFrequency (%)
D 4971
31.9%
U 4970
31.9%
S 4937
31.7%
C 162
 
1.0%
N 155
 
1.0%
Y 155
 
1.0%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 4971
31.9%
U 4970
31.9%
S 4937
31.7%
C 162
 
1.0%
N 155
 
1.0%
Y 155
 
1.0%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 4971
31.9%
U 4970
31.9%
S 4937
31.7%
C 162
 
1.0%
N 155
 
1.0%
Y 155
 
1.0%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 4971
31.9%
U 4970
31.9%
S 4937
31.7%
C 162
 
1.0%
N 155
 
1.0%
Y 155
 
1.0%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Total assets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5030
Distinct (%)99.0%
Missing160
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1.5659815 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:23.866752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3674254
Q117236983
median46916300
Q31.2131675 × 108
95-th percentile5.6981996 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.0407977 × 108

Descriptive statistics

Standard deviation5.2257139 × 108
Coefficient of variation (CV)3.3370215
Kurtosis239.24707
Mean1.5659815 × 108
Median Absolute Deviation (MAD)36443350
Skewness12.74809
Sum7.9583178 × 1011
Variance2.7308085 × 1017
MonotonicityNot monotonic
2024-08-12T14:15:23.969869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41590200 38
 
0.7%
144976992 2
 
< 0.1%
106514000 2
 
< 0.1%
14000000 2
 
< 0.1%
28483600 2
 
< 0.1%
100625000 2
 
< 0.1%
37457200 2
 
< 0.1%
69067000 2
 
< 0.1%
21921800 2
 
< 0.1%
46608800 2
 
< 0.1%
Other values (5020) 5026
95.9%
(Missing) 160
 
3.1%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8910760000 1
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5004
Distinct (%)96.8%
Missing72
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean-10.383446
Minimum-27482.559
Maximum11880.286
Zeros5
Zeros (%)0.1%
Negative2446
Negative (%)46.7%
Memory size41.1 KiB
2024-08-12T14:15:24.190338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-27482.559
5-th percentile-8.815715
Q1-1.1955
median0.0831
Q32.046275
95-th percentile9.05352
Maximum11880.286
Range39362.844
Interquartile range (IQR)3.241775

Descriptive statistics

Standard deviation553.6013
Coefficient of variation (CV)-53.315758
Kurtosis1982.8002
Mean-10.383446
Median Absolute Deviation (MAD)1.55655
Skewness-39.023904
Sum-53682.416
Variance306474.4
MonotonicityNot monotonic
2024-08-12T14:15:24.288561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.2935 38
 
0.7%
0 5
 
0.1%
0.6003 3
 
0.1%
-0.0212 3
 
0.1%
-0.0396 3
 
0.1%
0.0198 3
 
0.1%
1.6269 2
 
< 0.1%
-0.3885 2
 
< 0.1%
-0.8787 2
 
< 0.1%
-0.1822 2
 
< 0.1%
Other values (4994) 5107
97.4%
(Missing) 72
 
1.4%
ValueCountFrequency (%)
-27482.5587 1
< 0.1%
-24710.4779 1
< 0.1%
-5086.0393 1
< 0.1%
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1952.8796 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
4380.6219 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%

Dividend Per Share Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1960
Distinct (%)77.1%
Missing2699
Missing (%)51.5%
Infinite0
Infinite (%)0.0%
Mean2.5680843
Minimum0
Maximum2950.8354
Zeros467
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:24.398384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.11335
median0.7221
Q31.6024
95-th percentile4.72019
Maximum2950.8354
Range2950.8354
Interquartile range (IQR)1.48905

Descriptive statistics

Standard deviation58.649084
Coefficient of variation (CV)22.837679
Kurtosis2515.1129
Mean2.5680843
Median Absolute Deviation (MAD)0.6847
Skewness50.022654
Sum6530.6384
Variance3439.7151
MonotonicityNot monotonic
2024-08-12T14:15:24.497143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
 
8.9%
0.2 4
 
0.1%
0.0002 4
 
0.1%
1 3
 
0.1%
0.04 3
 
0.1%
0.2401 3
 
0.1%
0.2402 3
 
0.1%
1.36 3
 
0.1%
0.24 3
 
0.1%
0.6149 3
 
0.1%
Other values (1950) 2047
39.0%
(Missing) 2699
51.5%
ValueCountFrequency (%)
0 467
8.9%
0.0001 1
 
< 0.1%
0.0002 4
 
0.1%
0.0003 1
 
< 0.1%
0.0004 2
 
< 0.1%
0.0005 1
 
< 0.1%
0.0008 2
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0012 1
 
< 0.1%
ValueCountFrequency (%)
2950.8354 1
< 0.1%
140 1
< 0.1%
134.0642 1
< 0.1%
26.5818 1
< 0.1%
25.0042 1
< 0.1%
23.5369 1
< 0.1%
20.335 1
< 0.1%
20.2984 1
< 0.1%
19.7907 1
< 0.1%
19.5652 1
< 0.1%

EBITDA CAGR (5y)
Real number (ℝ)

MISSING  SKEWED 

Distinct1998
Distinct (%)80.8%
Missing2769
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean85.115682
Minimum-81.41
Maximum189631.11
Zeros0
Zeros (%)0.0%
Negative735
Negative (%)14.0%
Memory size41.1 KiB
2024-08-12T14:15:24.602365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-81.41
5-th percentile-21.392
Q1-2.11
median6.42
Q316.04
95-th percentile41.608
Maximum189631.11
Range189712.52
Interquartile range (IQR)18.15

Descriptive statistics

Standard deviation3813.1647
Coefficient of variation (CV)44.79979
Kurtosis2472.8322
Mean85.115682
Median Absolute Deviation (MAD)9.14
Skewness49.726739
Sum210491.08
Variance14540225
MonotonicityNot monotonic
2024-08-12T14:15:24.703465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.62 6
 
0.1%
-8.31 5
 
0.1%
-3.64 4
 
0.1%
5.74 4
 
0.1%
5.66 4
 
0.1%
1.65 4
 
0.1%
4.45 4
 
0.1%
8.44 4
 
0.1%
4.37 4
 
0.1%
11.65 4
 
0.1%
Other values (1988) 2430
46.4%
(Missing) 2769
52.8%
ValueCountFrequency (%)
-81.41 1
< 0.1%
-75.69 1
< 0.1%
-64.67 1
< 0.1%
-58.66 1
< 0.1%
-55 1
< 0.1%
-54.22 1
< 0.1%
-51.79 1
< 0.1%
-49.8 1
< 0.1%
-48.62 1
< 0.1%
-48.04 1
< 0.1%
ValueCountFrequency (%)
189631.11 1
< 0.1%
248.75 1
< 0.1%
211.04 1
< 0.1%
208.13 1
< 0.1%
160.43 1
< 0.1%
137.53 1
< 0.1%
135.05 1
< 0.1%
130.94 1
< 0.1%
127.74 1
< 0.1%
123.28 1
< 0.1%

EBITDA
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4054
Distinct (%)98.7%
Missing1134
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean-6.0203895 × 109
Minimum-3.0704371 × 1013
Maximum1.717253 × 1012
Zeros0
Zeros (%)0.0%
Negative1621
Negative (%)30.9%
Memory size41.1 KiB
2024-08-12T14:15:24.813716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.0704371 × 1013
5-th percentile-1.9559475 × 108
Q1-17992500
median32426000
Q34.27493 × 108
95-th percentile4.21065 × 109
Maximum1.717253 × 1012
Range3.2421624 × 1013
Interquartile range (IQR)4.454855 × 108

Descriptive statistics

Standard deviation4.798668 × 1011
Coefficient of variation (CV)-79.706937
Kurtosis4081.0513
Mean-6.0203895 × 109
Median Absolute Deviation (MAD)1.1092641 × 108
Skewness-63.766506
Sum-2.473176 × 1013
Variance2.3027215 × 1023
MonotonicityNot monotonic
2024-08-12T14:15:24.916221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-195594752 38
 
0.7%
1488999936 2
 
< 0.1%
-21442000 2
 
< 0.1%
102000000 2
 
< 0.1%
450000000 2
 
< 0.1%
280700000 2
 
< 0.1%
-274000000 2
 
< 0.1%
2894000128 2
 
< 0.1%
728000000 2
 
< 0.1%
-56512000 2
 
< 0.1%
Other values (4044) 4052
77.3%
(Missing) 1134
 
21.6%
ValueCountFrequency (%)
-3.070437097 × 10131
< 0.1%
-1.942801613 × 10101
< 0.1%
-7924198912 1
< 0.1%
-7255211008 1
< 0.1%
-4800000000 1
< 0.1%
-4107000064 1
< 0.1%
-2722977024 1
< 0.1%
-2179000064 1
< 0.1%
-2116557952 1
< 0.1%
-1929326208 1
< 0.1%
ValueCountFrequency (%)
1.717253046 × 10121
< 0.1%
1.832219935 × 10111
< 0.1%
1.349113037 × 10111
< 0.1%
1.317810012 × 10111
< 0.1%
1.29433002 × 10111
< 0.1%
1.154780037 × 10111
< 0.1%
1.040490004 × 10111
< 0.1%
7.817014477 × 10101
< 0.1%
7.477400371 × 10101
< 0.1%
7.090299699 × 10101
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct3661
Distinct (%)71.2%
Missing101
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-62.676605
Minimum-108880
Maximum12633.08
Zeros5
Zeros (%)0.1%
Negative2358
Negative (%)45.0%
Memory size41.1 KiB
2024-08-12T14:15:25.021397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-150.52
Q1-22.79
median1.19
Q38.3
95-th percentile24.04
Maximum12633.08
Range121513.08
Interquartile range (IQR)31.09

Descriptive statistics

Standard deviation1700.8773
Coefficient of variation (CV)-27.137356
Kurtosis3349.2796
Mean-62.676605
Median Absolute Deviation (MAD)10.53
Skewness-54.814428
Sum-322220.43
Variance2892983.7
MonotonicityNot monotonic
2024-08-12T14:15:25.123168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.16 38
 
0.7%
4.53 8
 
0.2%
6.15 7
 
0.1%
4.19 7
 
0.1%
1.17 6
 
0.1%
1.32 6
 
0.1%
9.32 6
 
0.1%
3.7 6
 
0.1%
3.2 6
 
0.1%
6.2 6
 
0.1%
Other values (3651) 5045
96.2%
(Missing) 101
 
1.9%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Ratio Debt/Equity (Annual)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3451
Distinct (%)67.6%
Missing139
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1.9511451
Minimum0
Maximum848.9286
Zeros1074
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:25.228533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00965
median0.3128
Q30.976
95-th percentile5.645
Maximum848.9286
Range848.9286
Interquartile range (IQR)0.96635

Descriptive statistics

Standard deviation15.927773
Coefficient of variation (CV)8.1632947
Kurtosis1713.3263
Mean1.9511451
Median Absolute Deviation (MAD)0.3128
Skewness36.327147
Sum9956.6937
Variance253.69395
MonotonicityNot monotonic
2024-08-12T14:15:25.326897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1074
 
20.5%
0.1031 38
 
0.7%
0.0002 10
 
0.2%
0.0001 9
 
0.2%
0.0005 7
 
0.1%
0.0006 6
 
0.1%
0.002 6
 
0.1%
0.0008 6
 
0.1%
0.0065 6
 
0.1%
0.004 6
 
0.1%
Other values (3441) 3935
75.1%
(Missing) 139
 
2.7%
ValueCountFrequency (%)
0 1074
20.5%
0.0001 9
 
0.2%
0.0002 10
 
0.2%
0.0003 2
 
< 0.1%
0.0004 5
 
0.1%
0.0005 7
 
0.1%
0.0006 6
 
0.1%
0.0007 2
 
< 0.1%
0.0008 6
 
0.1%
0.0009 1
 
< 0.1%
ValueCountFrequency (%)
848.9286 1
< 0.1%
439.3673 1
< 0.1%
262.3333 1
< 0.1%
255.8918 1
< 0.1%
181.8148 1
< 0.1%
165.6711 1
< 0.1%
136.576 1
< 0.1%
114.4921 1
< 0.1%
102.7736 1
< 0.1%
99.25 1
< 0.1%

Dividend Yield Indicated Annual
Real number (ℝ)

MISSING 

Distinct1723
Distinct (%)96.4%
Missing3454
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean3.6397948
Minimum0
Maximum37.974686
Zeros52
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-08-12T14:15:25.427675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29014166
Q11.3074151
median2.6693502
Q34.4594362
95-th percentile11.382296
Maximum37.974686
Range37.974686
Interquartile range (IQR)3.1520212

Descriptive statistics

Standard deviation3.6877001
Coefficient of variation (CV)1.0131615
Kurtosis13.639232
Mean3.6397948
Median Absolute Deviation (MAD)1.5035975
Skewness2.8080064
Sum6507.9531
Variance13.599132
MonotonicityNot monotonic
2024-08-12T14:15:25.530339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
1.0%
2.3501763 2
 
< 0.1%
3.937008 2
 
< 0.1%
9.208103 2
 
< 0.1%
1.7857141 2
 
< 0.1%
3.0464585 2
 
< 0.1%
0.44444445 2
 
< 0.1%
3.508772 2
 
< 0.1%
5.0377836 2
 
< 0.1%
1.7621145 2
 
< 0.1%
Other values (1713) 1718
32.8%
(Missing) 3454
65.9%
ValueCountFrequency (%)
0 52
1.0%
0.01611344 1
 
< 0.1%
0.02446483 1
 
< 0.1%
0.02486 1
 
< 0.1%
0.03518154 1
 
< 0.1%
0.0404408 1
 
< 0.1%
0.058823526 1
 
< 0.1%
0.061462812 1
 
< 0.1%
0.08841733 1
 
< 0.1%
0.09412973 1
 
< 0.1%
ValueCountFrequency (%)
37.974686 1
< 0.1%
36.06557 1
< 0.1%
33.41772 1
< 0.1%
27.79828 1
< 0.1%
25.14142 1
< 0.1%
22.988506 1
< 0.1%
21.467888 1
< 0.1%
20.3789 1
< 0.1%
19.793814 1
< 0.1%
19.59184 1
< 0.1%

Interactions

2024-08-12T14:15:15.823978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:40.952545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:42.746763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.739326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.457754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.187617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:49.966528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:51.855626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.642906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.485492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.349473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.152638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.028013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:02.859702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.661877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.544179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.465743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.381123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.233941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.099319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.010890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.055997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:42.838396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.817575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.538371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.264725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.047945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:51.939679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.721851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.568161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.434453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.233889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.109512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:02.940466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.847625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.641560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.551887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.465097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.315353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.175126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.107234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.142262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:42.933795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.900705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.626735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.350939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.137817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.035117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.812560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.658142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.527587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.323377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.200028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:03.030067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.936475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.729728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.648882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.558469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.407499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.263574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.200420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.220233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:43.021487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.975566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.709778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.429691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.217840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.112913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.894141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.735941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.604866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.401553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.282149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:03.111059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:05.020662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.816775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.734313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.646216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.491266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.341701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.287293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.300995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:43.116582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:45.061317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.794822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.515309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.305812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.205743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.977757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.819493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.694733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.488234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.364344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:03.202704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:05.112347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.896284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.826467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.736996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.583450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.426457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.375472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.377895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:43.209838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:45.139485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.878129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.591505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.388650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.288926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:54.061623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.904541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.779601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.576477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.448097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:03.286484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:05.198315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.983055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.913853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.824470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.667825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.505281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.474101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.466476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:43.302022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:45.225806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.968484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.680656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.497108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.385011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:54.251832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.998273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.875914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.771864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:01.538313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:03.381413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:05.294536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:07.080043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:09.015565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.921186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.762810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.596089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:16.561836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:41.561719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:43.487573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:45.301624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:47.059425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.864087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:50.587266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:52.476478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-08-12T14:14:57.093256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:58.891971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:00.773698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:02.608208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.397098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.286585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.187627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.017844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:11.958887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:13.847938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:15.563737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:17.579366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:42.571299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.562033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.289998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.018290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:49.800254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:51.670932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.470583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.315561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.177385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:58.979253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:00.859198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:02.690836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.483180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.372522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.285211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.203276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.048816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:13.929551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:15.649718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:17.668527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:42.647499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:44.646450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:46.368865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:48.098364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:49.877331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:51.758179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:53.555063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:55.394599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:57.257041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:14:59.064357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:00.939851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:02.768783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:04.567454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:06.456966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:08.369938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:10.286437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:12.133733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:14.010094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T14:15:15.732468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T14:15:25.742675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaChiffre d'affairesCurrencyDividend Per Share AnnualDividend Yield Indicated AnnualEBITDAEBITDA CAGR (5y)EPS AnnualExchangeMarket Cap (in M)P/E RatioPerformance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRatio Debt/Equity (Annual)Résultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.875-0.0150.6060.0000.482-0.4880.5680.2500.5350.0060.7160.2120.3400.8880.9630.4770.1980.5270.0000.2080.3010.243
52 Weeks Low0.8751.000-0.1130.6730.0000.540-0.4350.6900.2390.6840.0050.8190.2170.4790.9800.9060.6040.2220.6500.0000.2770.2240.149
Beta-0.015-0.1131.000-0.0510.047-0.105-0.107-0.2530.031-0.2350.1550.0450.114-0.065-0.077-0.058-0.257-0.005-0.2400.1280.1580.2540.274
Chiffre d'affaires0.6060.673-0.0511.0000.9980.329-0.2850.7930.0890.5520.0000.8130.0990.2830.6690.6290.4880.3980.5250.0000.6110.5450.508
Currency0.0000.0000.0470.9981.0000.9970.0000.9980.0000.7070.0630.0000.0000.0000.0000.0000.0000.0000.9980.0630.0000.0000.000
Dividend Per Share Annual0.4820.540-0.1050.3290.9971.0000.2200.5270.0880.5100.0000.448-0.0540.1340.5100.5050.2990.1640.4840.0000.2160.1730.179
Dividend Yield Indicated Annual-0.488-0.435-0.107-0.2850.0000.2201.000-0.085-0.188-0.2950.022-0.303-0.282-0.233-0.475-0.446-0.2680.151-0.2530.134-0.037-0.065-0.053
EBITDA0.5680.690-0.2530.7930.9980.527-0.0851.0000.1130.7400.0000.6820.0050.3900.6700.6110.6780.3960.7890.0000.3880.3150.269
EBITDA CAGR (5y)0.2500.2390.0310.0890.0000.088-0.1880.1131.0000.3570.0000.176-0.1050.1420.2520.2290.372-0.0310.2680.0000.0170.0450.032
EPS Annual0.5350.684-0.2350.5520.7070.510-0.2950.7400.3571.0000.0210.580-0.3690.3850.6600.5760.8250.1610.8170.0180.2380.1290.078
Exchange0.0060.0050.1550.0000.0630.0000.0220.0000.0000.0211.0000.0400.0000.0480.0190.0000.0140.0000.0000.4000.0550.0000.000
Market Cap (in M)0.7160.8190.0450.8130.0000.448-0.3030.6820.1760.5800.0401.0000.2390.4920.8370.7200.4900.3080.5490.0260.7160.6030.553
P/E Ratio0.2120.2170.1140.0990.000-0.054-0.2820.005-0.105-0.3690.0000.2391.0000.1930.2380.209-0.4110.024-0.0920.0420.1040.1090.097
Performance (52 weeks)0.3400.479-0.0650.2830.0000.134-0.2330.3900.1420.3850.0480.4920.1931.0000.5660.2350.3290.0930.4000.0190.1810.0580.010
Price0.8880.980-0.0770.6690.0000.510-0.4750.6700.2520.6600.0190.8370.2380.5661.0000.8910.5780.2250.6280.0000.2880.2520.175
Price 52 Weeks Ago0.9630.906-0.0580.6290.0000.505-0.4460.6110.2290.5760.0000.7200.2090.2350.8911.0000.5150.2200.5590.0210.2100.2830.214
ROI Annual0.4770.604-0.2570.4880.0000.299-0.2680.6780.3720.8250.0140.490-0.4110.3290.5780.5151.0000.1070.7030.0430.1760.0590.032
Ratio Debt/Equity (Annual)0.1980.222-0.0050.3980.0000.1640.1510.396-0.0310.1610.0000.3080.0240.0930.2250.2200.1071.0000.1760.0170.2730.2540.239
Résultat net0.5270.650-0.2400.5250.9980.484-0.2530.7890.2680.8170.0000.549-0.0920.4000.6280.5590.7030.1761.0000.0000.2180.1520.104
Sector0.0000.0000.1280.0000.0630.0000.1340.0000.0000.0180.4000.0260.0420.0190.0000.0210.0430.0170.0001.0000.0450.0000.019
Total assets0.2080.2770.1580.6110.0000.216-0.0370.3880.0170.2380.0550.7160.1040.1810.2880.2100.1760.2730.2180.0451.0000.7650.771
Volume 1 month0.3010.2240.2540.5450.0000.173-0.0650.3150.0450.1290.0000.6030.1090.0580.2520.2830.0590.2540.1520.0000.7651.0000.936
Volume 52 weeks0.2430.1490.2740.5080.0000.179-0.0530.2690.0320.0780.0000.5530.0970.0100.1750.2140.0320.2390.1040.0190.7710.9361.000

Missing values

2024-08-12T14:15:17.831280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T14:15:18.153257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-12T14:15:18.531726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
0TRNSTranscat Inc116.29001051.55269274.81570.9319895.090984e+047.298288e+04147.11584.450NASDAQ0.211360US2.655900e+0815106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.6340NaN14.8836794000.05.950.0185NaN
1ACRVAcrivon Therapeutics Inc7.1800217.991134NaN0.7118532.340076e+057.426779e+0412.8503.190NASDAQ-0.381850USNaN-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352NaNNaN-69743000.0-49.830.0000NaN
2COLMColumbia Sportswear Co81.83504781.35748718.83610.6283734.619039e+055.568954e+0587.23066.010NASDAQ0.098148US3.385903e+09227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.09291.20301.65421724992.012.970.00001.491239
3ZCMDZhongchao Inc1.490012.008726NaN-1.4798833.614145e+051.164410e+0612.0001.000NASDAQ-0.873369CN1.943394e+07-11335911.0HealthcareHealth Information Services11.700000USDNaN-4.3550NaNNaN-4902353.0-62.950.0000NaN
4MOVEMovano Inc0.371436.525224NaN1.2164911.513081e+052.487234e+051.4000.266NASDAQ-0.715289US8.520000e+05-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339NaNNaN-28069000.0-837.140.0142NaN
5NMIHNMI Holdings Inc37.24002955.0584249.12950.6787915.305636e+055.543996e+0542.00025.640NASDAQ0.280151US6.179140e+08348496992.0Financial ServicesInsurance - Specialty29.110001USDNaN3.8413NaN23.30496424000.013.860.2064NaN
6GNTAGenenta Science SPA4.563581.703059NaN-1.1924995.514391e+032.803921e+046.1002.200NASDAQ-0.199875ITNaN-11645455.0HealthcareBiotechnology5.700000EURNaN-0.6393NaNNaN-11690489.0-57.000.0000NaN
7ALLRAllarity Therapeutics Inc0.14266.204360NaN3.2902102.291709e+069.179877e+0646.6000.138NASDAQ-0.996625USNaN-12823000.0HealthcareBiotechnology41.599998USDNaN-119.6080NaNNaN-16895000.0-285.881.9720NaN
8RRBIRed River Bancshares Inc49.3300341.0500259.66820.6513409.616577e+031.154975e+0458.00042.780NASDAQ-0.004805US1.049270e+0832488000.0Financial ServicesBanks - Regional49.567513USDNaN4.85660.32038.47NaN11.480.00000.736950
9HCKTHackett Group Inc25.5550711.35649220.49760.4041379.765614e+041.270293e+0527.68020.230NASDAQ0.090217US2.974240e+0834749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.23570.44207.9359527000.027.810.36311.707412
SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
5232IVZInvesco Ltd16.16007272.524128NaN1.2013634.633188e+064.554791e+0618.2812.4800NYSE0.027753US5.814000e+09-337200000.0Financial ServicesAsset Management15.724797USD450032000.0-0.21311.3150NaN1.050800e+09-0.420.58995.209657
5233FBPFirst BanCorp19.74003285.27102510.84730.9282111.117185e+061.213457e+0622.1212.7150NYSE0.347341PR8.751180e+08310807008.0Financial ServicesBanks - Regional14.663054USD163864992.01.70940.566315.24000NaN18.250.10803.258656
5234SNDASonida Senior Living Inc29.3600418.113320NaN0.8845681.800198e+042.690000e+0434.266.8900NYSE1.992714US2.395880e+08-23029000.0HealthcareMedical Care Facilities9.840000USD14240900.0-3.11040.0000-1.160002.636500e+07-3.8064.9138NaN
5235MSDMorgan Stanley Emerging Markets Debt Fund Inc7.6000153.450000NaNNaN8.012738e+047.786522e+047.766.1100NYSE0.309895US1.492400e+0716991000.0Financial ServicesAsset Management5.806285USD20190300.0-1.8436NaNNaNNaN-24.250.0052NaN
5236COHRCoherent Corp63.34009656.880542NaN3.4231482.326590e+062.209304e+0680.9128.4700NYSE0.403250US4.598377e+09-414567008.0TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859NaN30.669277.353950e+08-2.240.5989NaN
5237TWLOTwilio Inc60.41009701.836400NaN1.5282492.782786e+062.422213e+0678.1649.8561NYSE-0.024610US4.239172e+09-594321984.0TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389NaNNaN3.837300e+07-9.460.1034NaN
5238NaNNano Labs Ltd0.282121.119193NaN0.8763902.915353e+055.139130e+044.750.2801NASDAQ-0.769699CN7.833538e+07-254352048.0TechnologySemiconductors1.220000CNY41590200.0-4.2935NaNNaN-1.955948e+0817.160.1031NaN
5239NaNNano Labs Ltd0.282121.119193NaN0.8763902.915353e+055.139130e+044.750.2801NASDAQ-0.769699CN7.833538e+07-254352048.0TechnologySemiconductors1.220000CNY41590200.0-4.2935NaNNaN-1.955948e+0817.160.1031NaN
5240NaNNano Labs Ltd0.282121.119193NaN0.8763902.915353e+055.139130e+044.750.2801NASDAQ-0.769699CN7.833538e+07-254352048.0TechnologySemiconductors1.220000CNY41590200.0-4.2935NaNNaN-1.955948e+0817.160.1031NaN
5241NaNNano Labs Ltd0.282121.119193NaN0.8763902.915353e+055.139130e+044.750.2801NASDAQ-0.769699CN7.833538e+07-254352048.0TechnologySemiconductors1.220000CNY41590200.0-4.2935NaNNaN-1.955948e+0817.160.1031NaN

Duplicate rows

Most frequently occurring

SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual# duplicates
0NaNNano Labs Ltd0.282121.119193NaN0.87639291535.3174651391.3043484.750.2801NASDAQ-0.769699CN78335376.0-254352048.0TechnologySemiconductors1.22CNY41590200.0-4.2935NaNNaN-195594752.017.160.1031NaN38